We note that the proliferation of U-net-based models still target the task of supervised learning: with a U-net, the output is directly in pixel space, rather than our model which outputs representations in a learned embedding space. Thus, this work is easily differentiated from all works focused on minor variations on the U-net architecture such as adding skip connections, attention layers, adaptive kernels,  [1]}, [2]}, [3]}, [4]}, [5]}.
